Non-peer reviewed preprint submitted to EarthArXiv

Closing the gap: Explaining persistent underestimation by US oil and natural gas
production-segment methane inventories
Jeffrey S. Rutherford1, Evan D. Sherwin1, Arvind P. Ravikumar2, Garvin A. Heath3, Jacob
Englander4, Daniel Cooley5, David Lyon6, Mark Omara6, Quinn Langfitt4, Adam R. Brandt1
1

Department of Energy Resources Engineering, Stanford University, Stanford CA 94305;
jruthe@stanford.edu (J.S.R.); evands@stanford.edu (E.D.S.); abrandt@stanford.edu (A.R.B.).
2
Department of Systems Engineering, Harrisburg University of Science and Technology,
Harrisburg, PA 17101; ARavikumar@HarrisburgU.edu.
3
National Renewable Energy Laboratory, Golden, CO 80401; Joint Institute for Strategic
Energy Analysis (JISEA); Garvin.Heath@nrel.gov.
4
Industrial Strategies Division, California Air Resources Board, Sacramento CA 95814;
jacob.englander@arb.ca.gov (J.G.E.); Quinn.Langfitt@arb.ca.gov (Q.L.).
5
Department of Statistics, Colorado State University, Ft. Collins CO 80523;
cooleyd@rams.colostate.edu.
6
Environmental Defense Fund, Austin TX; dlyon@edf.org (D.L.); momara@edf.org (M.O.).
Abstract
Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor
to greenhouse gas emissions. In the United States (US), recent synthesis studies of fieldmeasurements of CH4 emissions at different spatial scales are ~1.5x-2x greater compared to
official Environmental Protection Agency (EPA) greenhouse gas inventory (GHGI) estimates.
Site-level field studies have isolated the production-segment as the dominant contributor to thisdivergence. Based on an updated synthesis of measurements from component-level field studies,
we develop a new inventory-based model for CH4 emissions using bootstrap resampling thatagrees within error with recent syntheses of site-level field studies and allows for isolation ofdifferences between our inventory and the GHGI at the equipment-level. We find that venting
and malfunction-related emissions from tanks and other equipment leaks are the largest
contributors to divergence with the GHGI. To further understand this divergence, we decompose
GHGI equipment-level emission factors into their underlying component-level data. This
decomposition shows that GHGI inventory methods are based on measurements of emission
rates that are systematically lower compared with our updated synthesis of more recent
measurements. If our proposed method were adopted in the US and other jurisdictions, inventory
estimates could become more accurate, helping to guide methane mitigation policy priorities.

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Methane (CH4) is the principle constituent of natural gas and is also a potent greenhouse gas(GHG) [1]. During production of oil and natural gas (O&NG), some processes are designed tovent CH4 to the air, and CH4 is also emitted unintentionally via leaks in the system. According tothe official United States (US) GHG inventory, CH4 from O&NG operations are estimated tocontribute ~3% of national GHG emissions (with 100 year GWP = 25, [2]). At the international
level the contribution is approximately 5% (based on estimates from [3] and [4]). However, theuncertainty in this estimate, data gaps, and inconsistency with alternative approaches suggested aneed for further evidence [5]–[8]. To this end, significant research in the past decade has
investigated CH4 emissions from the O&NG system.
The US Environmental Protection Agency (EPA) estimates O&NG CH4 emissions in an annual
Greenhouse Gas Inventory (GHGI) [9]. The GHGI uses a data-rich, “bottom-up” approach toestimate national CH4 emissions by scaling up CH4 emissions measurements from activities likewell completions and gas handling components like valves or seals. However, a recurrent theme
consistently found in the literature is that the GHGI underestimates total US O&NG CH4emissions compared to observed values [10]. Brandt et al. [11] summarize the literature, andobserve that national-scale estimates from large-scale field studies exceed the GHGI by ~1.5
times. This difference is sometimes referred to as the “top-down/bottom-up” gap [11]–[17],based on the differences in approach between the GHGI and the conflicting studies. “Top-down”
studies determine total emissions from multiple sites via measurements from aircraft, satellites,or weather stations (e.g. [14]–[16], [18]–[20]).
Some recent studies have used a meso-scale “site-level” approach which measures CH4 downwind of facilities (e.g., well-pads) to estimate total emissions of an entire site or facility (e.g.
[21]–[24]). A recent synthesis of site-level data by Alvarez et al. [13] finds agreement between
site-level results and top-down results, with a best estimate of supply chain emissions (including
all equipment from production to distribution) ~1.8 times that of the component-level GHGI [25](up to ~2.1x in the production-segment).
Most emissions sources in the GHGI are derived using bottom-up methods. The bottom-up
approach estimates overall CH4 emissions by combining counts of individual components (oractivities) with emissions per component/activity (the “emission factor”). The bottom-up
approach allows for representation of sources at a high resolution, with 67 and 45 separate
sources for the O&NG production segments, respectively [25]. Because of this high resolution,
the GHGI is useful for development of CH4 mitigation policies. For example, the Obamaadministration’s Climate Action Plan developed recommendations using the relative contributionof emissions sources in the GHGI [26]. Also, the bottom-up framework of the GHGI isrecommended for reporting national emissions under the United Nations Framework Convention
on Climate Change (UNFCCC, [27]), under which participating countries report their inventoryof GHG emissions.
This study aims to answer two questions. First, why does the bottom-up EPA GHGI
underestimate CH4 emissions compared to both site-level and large-scale top-down studies?
Second, is this underestimation due to an inherent problem with the bottom-up methods used in
the GHGI? Previous studies have noted that the underlying data sources of the GHGI were2

Non-peer reviewed preprint submitted to EarthArXiv

published in the 1990s and may be outdated [11], [28], [29]. The site-level synthesis study ofAlvarez et al. [13] suggested that the divergence is likely due to a systematic bias in the bottomup methodology that misses “super-emitters”, a finding supported by others (e.g., [11] [30]).
Recent work suggests that top-down measurement campaigns are capturing systematically higher
emissions during daytime hours from episodic events. However, this may not be true at anational level, as it has been noted that the upward bias of top-down measurements was likely
explained by unusually high liquids-unloadings in the Fayetteville shale [13], [31]. Some haveattempted to construct alternative inventories (e.g., [13], [32], [33]), however these attempts havenot taken full advantage of the robust set of component-level data now available.
In this study, for the first time, we explain with source-level specificity the underestimation of
O&NG CH4 emissions in the GHGI as compared to top-down studies. Our analysis boundary is
the O&NG production segment which includes all active, onshore well pads and tank batteries(excluding inactive and offshore wells) and ends prior to centralized gathering and processing
facilities (Figure S1). We focus on the production segment given its significant emissions (~58%
of total supply chain CH4 emissions in Alvarez et al. [13]) and the large difference between sitelevel estimates and the GHGI [13] (~70% of difference between Alvarez et al. [13] and the
GHGI, Figure S2). This study develops and validates approaches that can be applied to othersegments in the O&NG supply chain.
Our novel contributions are threefold. First, we construct a bottom-up, O&NG productionsegment CH4 emissions estimation tool based on the most comprehensive public database ofcomponent-level activity and emissions measurements yet assembled. Our approach differs fromthe GHGI in that it applies modern statistical approaches (bootstrap resampling) to allow for
inclusion of infrequent, large emitters, thus robustly addressing the issue of super-emitters.
Second, we use this tool to produce an inventory of US O&NG production segment CH4
emissions and compare this with the GHGI and previous site-level results, showing that much of
the divergence between different methods at different scales vanishes when we apply our
improved dataset and statistical approaches. As mentioned earlier, site-level synthesis studies
have been validated against even larger-scale top-down studies, so improved alignment between
our method and site-level results suggests much better agreement with top-down results [13],[34]. Third, to isolate specific sources of disagreement between the GHGI and other studies, wereconstruct the GHGI emission factors beginning with the underlying datasets and uncover somepossible sources of disagreement between inventory methods and top-down studies. Based onthese results, we suggest a strategy for improving the accuracy of the GHGI, and likewise any
country using a similar approach in reporting O&NG CH4 emissions to the UNFCCC.

A new bottom-up approach
Bottom-up approaches extrapolate component or equipment emissions rates to large (e.g.,national) scales by multiplying emission factors (emissions per component or equipment per unittime) by activity factors (counts of components per equipment, and equipment per well) (Figure1). Our estimation tool requires two sequential extrapolations, first from the component to theequipment-level, and second from the equipment to the national or regional-level.
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The approach utilized in our bottom-up estimation tool begins with a database of componentlevel direct emissions measurements (e.g., component-level emission factors). We generatecomponent-level emission factor distributions for this study from a literature review building onprior work [11], [30] and adding new publicly available quantified measurements (Table 1 in
Methods). Our resulting tool’s database includes ~3200 measurements from 6 studies across a
12-fold component classification scheme (see SI-3.2 for further description of this classification
scheme). We applied emission factors as reported in the individual studies, with no modifications
beyond unit conversion (noting that there are some differences between studies in High Flow
Sampler bias correction for gas concentration and flow rate, which may introduce uncertainty to
our results). Data for component counts and fraction of components emitting (the ratio of
emitting components to all components counted) was scarce, with only 3 studies containing
useful information for both ([35]–[37] for component counts and [35], [36], [38] for fraction ofcomponents emitting).
We derive equipment-level emission factors for our tool by random re-sampling (i.e.,bootstrapping, with replacement) from our component-level database according to component
counts per equipment and fraction of components emitting. Source-specific approaches wererequired for infrequent events (i.e., completions, workovers, liquids unloadings), methane slip
from reciprocating engines, and liquid storage tanks (see SI-3.3).
We then perform a second extrapolation, using our equipment-level emission and activity factors
to calculate a 2015 US O&NG production-segment CH4 emissions estimate. For this step, ourtool is integrated into the Oil Production and Greenhouse Gas Emissions Estimator (further
description of OPGEE can be found in SI-3.1) and parameterized using 2015 domestic wellcount and O&NG production data (same dataset as Alvarez et al. [13]). A total of ~1 million
wells and associated equipment are partitioned and analyzed across 74 analysis bins (SI-4.1). We
performed a Monte Carlo uncertainty analysis repeating the bootstrapping algorithm 100 times
across all ~1 million wells (SI-4.4). It is worth mentioning that emission factors are often
themselves only measured in a few locations, and thus in our extrapolation we assume
applicability to other regions.

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Figure 1: Schematic of this study’s bottom-up CH4 emissions estimation tool which involves
multiplication of emission factors (e.g., emissions per valve) by activity factors (e.g., number of valves
per wellhead). Two sequential extrapolations are performed using an iterative bootstrapping approach.
First, our database of component-level (e.g., valve, connector) emissions measurements (a) is
extrapolated using component-level activity factors to generate equipment-level (e.g., wellhead,separator) emission factors (b). Second, these equipment-level emission factor distributions are
extrapolated using equipment-level activity factors to generate a 2015 US O&NG production-segment
CH4 emissions estimate. This extrapolation is performed 100 times to generate a distribution of nationallevel CH4 emissions (c) and estimate a 95% confidence interval (CI).

Comparison of US production-segment CH4 emissions with site-level studies
and the GHGI
We first compare our resulting US 2015 O&NG production-segment CH4 emissions estimate
with the GHGI’s estimate for 2015 produced in their most recent 2020 inventory [25]. We also
validate our bottom-up tool by comparing total emissions and emissions distributions with thosegenerated in site-level synthesis studies (total emissions are compared with Alvarez et al. [13],site-level distributions are compared with Omara et al.[34]).
We estimate mean O&NG production-segment CH4 emissions of 6.3 Tg/yr (5.8-6.9 Tg/yr, at
95% confidence-interval, CI) (Fig. 2a, Note that the CI is relatively narrow given that this only
captures uncertainty due to resampling). Our mean, production-normalized emissions rate fromthe production segment is 1.3% (1.2-1.4% at 95% CI, based on gross NG production of 32
trillion cubic feet and an average CH4 content of 82% [39], [40]), slightly lower than Alvarez etal.[13], [34], who estimate 1.5% (applying the same denominator as above). Both our bottom-up
component-level inventory results and the Alvarez site-level results are approximately 2x thoseof the GHGI estimate of 3.6 Tg/yr (year 2015 data [25], excludes offshore systems) for the5Non-peer reviewed preprint submitted to EarthArXiv

O&NG production segment. Interestingly, the difference in US production-segment emissions
between this study and the GHGI is approximately the same volume as our estimate ofcontribution from super-emitters (top 5% of emissions events). Given that our results match theAlvarez et al. site-level results, we conclude that the divergence between the GHGI and topdown/site-level studies is not likely to be due to any inherent issue with the bottom-up approach.
Figure 2(b-c) show that site-level distributions developed using our model match empirical

distributions from the site-level synthesis study of Omara et al. [34]. To report our results on abasis consistent with site-level studies (recalling that sites can contain more than one well), wecluster equipment-level emissions outputs into production sites (SI-4.3). Several otherobservations from our simulations are of interest. First, our modeled emissions per site are higher
at liquids-rich sites versus gas-rich sites (Figure S29), in alignment with recent field measurement
campaigns in both Canada and the United States [41], [42]. Second, our model recreates thetrend demonstrated by Omara et al. wherein low-producing sites exhibit higher productionnormalized emissions rates [34] (Figure S30). Finally, the tail of our modeled distribution closely
matches the tail of the empirical Omara et al. distribution (Figure 2b and Figure S28). This is ofparticular interest, given that recent papers assert the divergence between the GHGI and sitelevel studies is mostly due to an inability of the bottom-up methods to capture super-emitters[32], [42]. Our results clearly show that a modern dataset with proper bootstrap resamplingtechniques can recreate observed super-emitters.
Because our approach uses a component-level, bottom-up approach, we can investigate thesource of differences with the GHGI. This cannot be done with site-level data. Relative to the
GHGI, contributions from equipment leaks in our estimate are larger by ~1.3 Tg CH4 and tankleaks and venting by ~2.1 Tg CH4 (Figure 3). Together, these two sources contribute over half oftotal O&NG production-segment CH4 emissions. The increase in estimated emissions fromequipment leaks compared to the GHGI are due to our updated emission factor; we know that thedifference is not due to equipment-level activity factors because ours are nearly identical to the
GHGI (see SI-2.3). In the next section we will perform a deeper investigation into bothcomponent-level emissions data for equipment leaks and tank modelling as underlying
contributors to differences between our results and the GHGI.

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Figure 2: (a) Comparison of this study’s aggregate US 2015 CH4 emissions from O&NG productionsegment with site-level results of Alvarez et al. (see Table S3 in [13] minus contributions from offshore
platforms and abandoned wells) and the GHGI [25] including fraction estimated from super-emitters(top 5% of sources) and 95% confidence interval. We also compare probability distributions of ourcomponent-level simulations (red lines), aggregated into site-level emissions, with site-level results ofOmara (blue line): (b) Cumulative distribution plot (CDF) describing the fraction of well-sites withemissions below a given amount, and (c) probability distribution of emissions rate per well-site with themean (filled square), median (x), and 95% confidence intervals shown above the plots. Results of thisstudy are presented using 100 Monte Carlo simulations. Because of the large number of sampled sites,
the Monte Carlo simulations all converge toward the same size distribution in panels (b) and (c).

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Figure 3: Contributions of emissions sources to our US 2015 O&NG production-segment inventory (and
95% confidence interval) compared with 2020 GHGI [25]. Inset pie charts illustrate individual sourcespecific contributions of our inventory to equipment leaks (left pie chart) and tanks (right pie chart).
Discrepancies with the GHGI are dominated by liquid hydrocarbon tank leaks and venting (“tanks”,~2.1 Tg/yr CH4) and equipment leaks (~1.3 Tg/yr CH4). Details regarding the modelling of tankemissions sources is given in SI-3.3. Results in tabular form are given in Table S2 and Table S3.

Main sources of GHGI underestimation
Given that our new component-level method is validated by the empirical results from site-level
field studies, can we explain why the GHGI produces lower O&NG production-segment CH4
emissions estimates? Results from our modelling (Figure 3), in addition to recent revisions by the
GHGI and other analyses (SI- 5.1), suggest that the downward bias of the GHGI is not due topneumatic controllers, liquids unloadings, or completions and workovers because either thedivergence is small or absolute emissions are small, or both. Methane slip in reciprocating
engines is higher in the GHGI, although the overall magnitude in difference is small. The
combustion emission factor used in the GHGI for methane slip from reciprocating gas engines isbased on a 1991 TRANSDAT dataset published by the Gas Research Institute [43]. The
difference compared to our study is probably explained by substantial improvement in engine
emissions since publication of that report (based on manufacturer reported specifications for8Non-peer reviewed preprint submitted to EarthArXiv

reciprocating gas engines [44]). For these reasons, this paper focuses its analysis of the twolargest sources of GHGI underestimation compared to our validated method: equipment leakage
and liquid hydrocarbon storage tanks, whose emissions are 1.3 and 2.1 Tg CH4 lower than ourestimates, respectively. See SI-1.1 for definitions of each emissions source.
The GHGI constructs emission factors for equipment-level leaks using an approach very similar
to ours, where emission factors of individual components are aggregated according to estimatedcounts of components per piece of equipment. To explore differences in equipment leakestimates, we decompose equipment-level emission factors into the constituent parts:
Component-level emissions data, component counts, and fraction of components emitting (therelationship between these parameters is defined in Figure 4).
Reconstructing equipment-level, equipment leakage emission factors from the GHGI iscomplicated by the fact that the underlying studies from the 1990s [35], [45] are at a moredetailed level than the GHGI itself. For example, the underlying data for natural gas systememission factors are subdivided by region (e.g., Western gas versus Eastern gas), and for
petroleum systems data are subdivided by product stream (e.g., light oil versus heavy oil).
Equipment-level emission factors for gas systems, for example, are a weighted average of bothWestern emission factors and Eastern emission factors. The GHGI approach to aggregating thesefactors to overall values for natural gas and petroleum systems is described in SI-5.2.
We demonstrate differences in equipment-level emission factors for equipment leaks via a
decomposition into constituent factors for a single example (equipment type and region) –leakage from gas wells in the West (Figure 4) – with equipment leaks from all other sources
similarly described in the SI (Figure S18 – Figure S26). The difference between our study’sequipment-level equipment leakage emission factor for Western natural gas wells and the GHGI
– the difference to be explained by decomposition – is ~5x (3.4 kg/day versus 0.7 kg/day). The
underlying factors are plotted in Figure 4.
First, we compare component-level emission factors, defined as the average emissions rate ofleaking components (Figure 4a). (Note that the “average emission rate of leaking components” isnot the same as an average emission rate for all components.) For Western gas and petroleum
systems in the GHGI, component-level leakage emission factors are constructed using a methodreferred to by the EPA [46] as the "EPA correlation approach” (defined in detail in SI-5.2.2). In
this approach, emission factors are constructed from a dataset of various facilities including oil
and gas production sites, refineries, and marketing terminals (n = 445, data compiled in the EPA
Protocol document [46]). The difference between our study’s component-level emission factors
and the GHGI for connectors, valves, and open-ended lines (the components comprising thewells) is ~7x, 6x, and 5x respectively (Figure 4a). Note that the decomposition in Figure 4a islimited to connectors, valves, and open- ended lines (the three components inventoried by the
GHGI) although our inventory also accounts for pressure relief valves, regulators, and other(miscellaneous) components on wells. The fact that GHGI equipment-level emission factors are
based upon only three component types (when more component classes exist, according to ourdatabase) will contribute to some underestimation.

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Figure 4b compares the fraction of components emitting (the ratio of emitting components to allcomponents counted), while Figure 4c shows component counts (number of components counted

per piece of equipment). These have offsetting effects, where component-level emission factors
and component counts contribute to higher emissions in our study versus the GHGI, and fractionof components emitting contributing to lower emissions in our study. The resulting total
emissions per well (Figure 4d) are the product of these factors, summed across all components.
Similar results are found across all equipment categories compared to the GHGI. In general, in
our dataset, component-level emission factors are higher [5x to 46x comparing our emission
factors for connectors, valves, and open-ended lines across all GHGI categories, see Figure S18 –Figure S26], the fraction of components emitting is lower [1x to 0.05x], and the number ofcomponents per piece of equipment is generally, but not always, higher [0.2x to 20x comparing
our emission factors for wells, separators, and meters across all GHGI categories, see Figure S18 –Figure S26]. Considering the decomposition presented here, along with the rest in the SI (Figure
S18 – Figure S26, plus some discussion of smaller factors not described here), we can explain muchof the overall underestimation of the GHGI compared to our results for the equipment leaks
source category.

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Figure 4: Example decomposition of the equipment-level emission factor for Western US gas wells(Note that units differ for each panel, and also the logarithmic scale meaning that visible differences
between points often span orders of magnitude). This study’s equipment-level emission factor (d) is
decomposed into constituent parts and compared with the GHGI. Constituent parts include:
component-level emission factors (a), fraction of components emitting (b), and component counts (c).
When multiplied together, these factors have counteracting biases, with component-level emission
factors and component counts contributing to higher emissions in our study versus the GHGI, andfraction of components emitting contributing to lower emissions in our study. Note that in actual usage
in the GHGI, equipment-level emission factors for gas systems are a weighted average of both Western
systems (API 4598, [47]) and Eastern gas systems (Star Environmental, [45]). Here, for illustration
purposes, we only show constituent data for Western gas systems; results for Eastern gas system arereported in SI Section 5.2. Further, we also limit this figure to connectors, valve, and open-ended lines(the three components inventoried by the GHGI) although our inventory also accounts for pressure
relief valves, regulators, and other (miscellaneous) components on wells.

The second source of significant divergence between this study and the GHGI for US CH4emissions in the O&NG production-segment is with emissions from liquid hydrocarbon storage
tanks. The EPA GHGI constructs storage tank emissions estimates using Greenhouse Gas
Reporting Program (GHGRP) data. The GHGRP is a program which collects emissions datafrom industrial facilities, where requirements for natural gas and petroleum systems are specified
by the Code of Federal Regulations Section 40 Subpart W [48]. Based on GHGRP data forstorage tanks (see methods in SI-5.3), we decompose total emissions for the GHGI into tankcounts and emission factors allowing us to draw comparisons to results from this study.
Before presenting our decompositions, it is worth noting two key differences in modelling ofemissions from liquid hydrocarbon storage tanks between our study and the GHGI (see further
description of how our model estimates tank emissions in SI-3.3.2). First, whereas our model isbased on direct measurements, the GHGI is based on operator reported simulations fromsoftware programs such as API E&P Tank or AspenTech HYSYS [49], [50]. Second, as aconsequence of these differing approaches, whereas our emissions are classified based on
measurement source (e.g., vent stack, thief hatch, etc.) GHGI emissions are classified according
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Non-peer reviewed preprint submitted to EarthArXivto the simulated process (e.g., flash emissions). As a consequence of these differences inemissions classification, comparisons between decompositions of our study versus the GHGI
will be imperfect.
With this in mind, we define emission factors in our decomposition as the summation of
intentional emission factors and unintentional emission factors (Figure 5). Here, intentional (flash
related) emission factors are based on direct emission measurements at the vent stack for ourstudy, and simulations of uncontrolled and controlled tanks in the GHGI (see details in SI-5.3).
Our comparison of unintentional emission factors is less precise. In the GHGI, unintentionalemissions are limited to what is reported under “malfunctioning separator dump valves”(although it is unclear if additional unintentional emissions are reported alongside flash
emissions in the other tank categories, see SI-5.3). Conversely, unintentional emission factors inour study are based on direct measurements of emissions from open thief hatches, rust-related
holes, and malfunctioning pressure-relief valves.
We demonstrate the decomposition in Figure 5 for petroleum systems (see Figure S28 in the SI for
natural gas systems). Note that flash emissions will only occur at controlled tanks, while
unintentional emissions from thief hatches, holes, or pressure-relief valves could occur at either
controlled or uncontrolled tanks. Figure 5 and Figure S28 demonstrate that, while several factors
contribute to differences, difference in emission factors for various unintentional emissions
sources are the greatest source of difference between this study and the GHGI. Unintentionalemission factors are the product of (i) average emissions rate per event, and (ii) frequency ofunintentional emissions events per tank. Both of these values are approximately an order ofmagnitude higher for our study as compared to the GHGI, contributing to the nearly two ordersof magnitude difference in total emissions.
Our findings suggest that both the magnitude and frequency of unintentional emissions sources
could contribute to significant underestimation in the GHGI. Due to the limited quantified,component-level data available on tank emissions (based upon safety and accessibility issues)
our tank emissions measurements come from a single study in a single geographic area (Eastern
Research Group in the Barnett shale,[51]). Therefore, more studies are required to provide acomprehensive view of tank emissions.
However, while quantified emissions data for tank sources are scarce, the existence of
unintentional emissions from tanks (due to open thief hatches, rust-related holes, pressure-relief
valves, etc.) has been corroborated by numerous ground and aerial surveys [42], [52]–[54].
Several of these studies are summarized in Table S26. Taken together, these studies provide
further evidence that: (i) high emissions events are frequently observed at storage tanks, not justfrom vents but also at open thief hatches and pressure relief valves, (ii) these high emissions
events are common at both controlled tanks and uncontrolled tanks, (iii) the frequency(events/tank) of unintentional emissions events is much higher than the rate suggested by the
EPA (2%, see Figure 5c) for malfunctioning separator dump valves.

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Figure 5: Decomposition of total emissions for oil tanks (far right panel) into constituent parts, withcomparison of this study’s dataset to those of the GHGI. From left to right: Total activity, intentional (flashrelated) emission factor, unintentional emission factor, and total emissions. Flash and unintentional emission
factors are decomposed into emission factors (kg CH4/ emitting tanks) and control rates (emitting tanks/ total
tanks). Note the log scale for the right three panels.

Discussion
Development of accurate inventories at the equipment-level is critical for targeting CH4
mitigation strategies. US government agencies [26], environmental groups [55], [56], andresearchers [57] rely on inventory data for policy design, cost analysis, formulation of leakdetection and repair programs, and life-cycle assessment research. However, recent studies haveemphasized a ~1.5x-2x divergence between the EPA GHGI estimates of CH4 emissions from
O&NG and those estimated from field measurements at different spatial scales. This suggests anopportunity for improvement in the GHGI approach.
In this study we develop a component-level, bottom-up approach validated by previous site-level
estimates of US 2015 CH4 emissions from the production segment of the O&NG sector.
Consistent with site-level findings, our estimate is ~1.8 times that of the GHGI. The strength of
our approach is that by developing our estimate using component-level data, we can diagnose at
the equipment-level the key sources contributing to the GHGI underestimation. Our detailed
decomposition identifies (i) underlying equipment-leak measurements and (ii) neglect of thecontribution of unintentional emissions events at tanks (e.g., liquid hydrocarbon tank “thiefhatches)” as the most important contributors to the underestimation.
These results demonstrate that the bottom-up methodology is a valid approach to produce
accurate emissions estimates and that improvements to inventory methods are possible. We makeseveral recommendations:13Non-peer reviewed preprint submitted to EarthArXiv

•

•

•

Improvements to equipment leak emission factors can be implemented relatively easily.
This study applies a very similar approach to the GHGI, albeit using a morecomprehensive set of data on component-level emission factors, fraction of components
emitting, and component counts. We can only speculate on why differences exist between
our dataset and the GHGI dataset, but based on the fact that our dataset is larger andcontains more recent measurements, we suggest that it is likely to be more representativeof today’s conditions.
Improvements to crude and condensate storage tank emission factors will be moredifficult. Differences between our emissions estimate and the emissions estimate of the
GHGI is believed to be largely a result of the GHGI neglecting emissions from failed
tank controls (e.g., open thief hatches). Although we attempt to estimate theircontribution, and reference supporting site-level surveys, a significant data gap exists inthis area.
Regular efforts to validate equipment-level emission factors by comparing existing or
new emission factors with measurements from randomly sampled sources at different
spatial scales would also improve accuracy and “build in” to inventory efforts the ability
to correct data over time.

The results of this study are also relevant globally. All parties to the UNFCCC submit annual
inventories, generated using a bottom-up approach, to report on progress towards GHG targets.
The IPCC’s Guidance Document on Emission factors outlines three approaches towards
producing an inventory, with the simplest approach (Tier 1) based on IPCC default emission
factors [27], [58]. Default emission factors for the petroleum and natural gas systems
production-segment are based upon the same underlying data sets as the GHGI. This means that,in addition to the US-submitted GHGI, other countries using Tier 1 emission factors will becontributing CH4 estimates according to data that we have found likely to be underestimating ofactual emissions, and thus the recommendations offered herein, if implemented, would improve
emissions estimates globally.
Improvements offered in this study are thus potentially directly applicable to the UNFCCC
inventory method and any country directly reporting emissions estimates to the UNFCCC. Our
study suggests an approach which can be applied to prepare a more accurate inventory.
.

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Methods:
Here, we describe the methodological aspects of each of this study’s three key contributions: (i)tool development, (ii) generating a US CH4 estimate for the O&NG production-segment, and (iii)
decomposing GHGI emission factors. Our methods are also described in greater detail in the
Supplementary Information (SI). Datasets and code are available in a Github repository:
https://github.com/JSRuthe/O-G_Methane_Supporting_CodeTool development
Tool structure
The analysis platform for this study is the methane venting and fugitives (VF) subroutine
embedded within the Oil and Gas Production Greenhouse Gas Emissions Estimator (OPGEE
version 3.0). This subroutine processes equipment-level emissions distributions and well andproduction values and produces gross emissions estimates.
The following equation describes the methane VF subroutine:
𝑛𝑓𝑖𝑒𝑙𝑑𝑛𝑤𝑒𝑙𝑙𝑠,𝑖 𝑛𝑒𝑞𝑢𝑖𝑝

𝑄𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 = ∑ { ∑ [ ∑ 𝐸𝐹𝑖,𝑗,𝑘 ∗ 𝑎𝑓𝑘 ]}
𝑖=1

𝑗=1

𝑘=1

Here, a “field” represents a subpopulation (or bin) of wells that share similar production
characteristics (e.g., gas to oil ratio). This binning was necessary because OPGEE generates
outputs (carbon intensity or methane leakage rate) on a “field” basis. For each field, i, emissions
are calculated well-by-well. For a single well, j, equipment-level emissions are calculated bymultiplying a randomly drawn emission factor, EFi,j,k [kg/equipment/day], by its respective
activity scaling factor, afk [# equipment/well]. Because we iterate across wells, there is no needto explicitly multiply the activity scaling factor by well count (see SI section 3.4). Emissions arecalculated across all equipment classes, k.
Database on component level studies
Our equipment-level emission factors are generated with a component-level measurement
database. We conducted a detailed literature review to inform the database for this study. This
review built on prior work done for Brandt et al. [11], [30] and adds new publicly available
component-level measurements. Studies were reviewed for information regarding: (i) data on
quantified emissions volumes per emitting component or source, (ii) activity counts for numbersof components per piece of equipment or per site, and (iii) data on fraction of components foundto be emitting in a survey.
Quantified emissions data was further filtered for: (i) data collected within the production(upstream) segment, (ii) and data collected in the United States (although we do include somecomponent count and fraction leaking data from Canada, see further details in SI-3.2). A total of
6 studies and ~ 3200 measurements met our inclusion criteria (see Table 1).

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Non-peer reviewed preprint submitted to EarthArXiv

To aggregate the data from the various studies, we developed a 12-category component
classification and 11-category equipment schemes. For components these include: Threaded
connections and flanges, valves, open-ended lines, pressure-relief valves, compressor seals,
regulators, pneumatic controllers/ actuators, chemical injection pumps, tank vents, tank thiefhatches, tank pressure-relief valves, and other (miscellaneous) components. For equipment theseinclude: Well, header, heater, separator, meter, tanks – leaks, tanks – vents, reciprocating
compressor, dehydrator, chemical injection pump, and pneumatic controller/actuator (note thatthe “tanks – leaks” category tracks all non-vent/hatch emissions on a tank (e.g., connectors,
valves, etc.) while the “tank – vent” category tracks all vent/hatch related emissions).
To align the categories of components used by the authors of a study to our common component
definitions, we create a set of “correspondence matrices” to perform consistent matrix
transformations (see SI-3.2.5).
Table 1: Oil and gas methane emission measurement studies that reported raw data for quantified
emissions measurements, fraction of components emitting, and component counts. These studies are asubset of all studies that were examined closely, meeting inclusion criteria described. Detailed summaryof each study’s results are reported in SI-6.

Study ID

Location

Number ofquantified
leaks

Number ofcomponents
screened

LeakvolumesusedComponent
counts used

Components screened

Allen 2013 [33]Various645
NR1
YN
Various components
Allen 2014 [59]Various
377
377
YN
Pneum. controllers
Bell 2017 [60]
Fayetteville
322
NR
Y
N
Various components
ERG 2011 [38]Barnett
1949
NR
Y
N
Various components
Thoma 2017
Uintah80
80
YN
Pneum. controllers[61]
Pasci 2019 [36]Various192
54,618Y
Y
Various components
API 1993 [35]Various4794
Y
Various components
182,833
N
Clearstone
CanadaN
Y
20181 [37]NR = not reported
1
Given that leakage data was taken in Canada, we limit usage of this data to component counts

In addition to component-level emissions measurements, we also require component counts andfraction of components emitting. A total of 3 studies contained information on component counts[35]–[37], and we aligned the data into our standard categories. Data on fraction of components
emitting was also scarce, with 3 studies containing useful information [35], [36], [51]. The
fraction emitting rate is an important parameter in deriving equipment-level emission factors, butvaries greatly by study due to (i) differences in screening methods between studies (e.g., Method
21 vs. infrared camera) and (ii) use of different screening sensitivity to assign a component to theemitting state (10 ppmv vs. 10,000 ppmv). Therefore, based on the technologies employed
different studies may be sampling different parts of the “true” population emissions distribution.
In order to ensure that we are not over or under-sampling a subset of the true distribution, we
split our dataset at 10,000 ppmv (see reasons for this threshold in SI-3.2.4). Different quantified
emissions bins and fraction emitting values were derived for the two halves.
16

Non-peer reviewed preprint submitted to EarthArXiv

Equipment-level emission factors
We required a variety of approaches to describe the different sources of emissions. The mostcommon approach taken by this study, utilized for fugitive leaks and most vents, is a “stochastic
failure” approach. In the stochastic failure approach we combine component-level emissions
data, component counts, and fraction emitting values to produce equipment-level emission
factors. These emission factors take the form of distributions which are generated by iteratively
resampling our emissions datasets (see SI-3.3.1).
For each equipment category, we iterate across component categories and draw emissions
measurements according to a probability specified by the fraction emitting value. Given that we
split our dataset at 10,000 ppmv (describing quantified emitters that were missed by optical gas
imaging but caught with Method 21 below the threshold, and emitters that were caught with
optical gas imaging above the threshold), we develop two sets of emission factors . These twoemission factor distributions are superposed to form our best approximation of the true emissions
distribution (SI-3.2.4).
We applied separate approaches for flashing emission from tanks, methane slip fromreciprocating compressors, and intermittent and startup losses from liquids unloading,completions, and workovers. These approaches are described in SI sections 3.3.2, 3.3.3, and3.3.4 respectively.
Equipment-level activity factors
In the GHGI, direct equipment counts are not available for every year. As an approximation, the
GHGI uses “activity drivers” such as gas production, number of producing wells, or systemthroughput. Activity drivers are multiplied by a scaling factor (e.g., separators per well) derived
from a subsample of the population. For each piece of equipment, we employ well counts as theactivity driver. Since the 2018 GHGI, the EPA has calculated activity factors for most equipmentusing scaling factors based on GHGRP data. Scaling factors based upon reporting year 2015
equipment counts are multiplied by year-specific wellhead counts to calculate year-specificequipment counts [62].

Extrapolation to US oil and gas wells
Development of representative “fields” for analysis
In OPGEE, fields are described with over 50 primary input parameters, and numerous secondary
parameters. Given that we are restricting our analysis to methane leaks and vents in the upstream
sector, however, we only concern ourselves with a handful of inputs: Oil production, well count,gas-to-oil ratio (GOR), and methane mole fraction. The 2015 well count and production data
(Table S31) were based on the dataset from Alvarez et al. [13], which were originally derived
from Enverus and filtered to remove offshore and inactive wells (~6,000).
In order to account for the heterogeneous nature of petroleum and NG systems, the total
population was divided into several simulation sub-populations (or “bins”) according to the
production GOR (where gas wells have a GOR > 100 mscf/bbl, [63]), gas productivity, and17

Non-peer reviewed preprint submitted to EarthArXiv

liquids unloadings method. 60 bins were developed for natural gas systems while 14 bins weredeveloped for petroleum systems (see SI-4.1).
Uncertainty analysis
This study applies the Monte Carlo method to estimate uncertainty. Input parameters –component-level emission factors, component counts, and fraction of components emitting – areassigned distributions, and the range of uncertainty in these distributions is propagated throughthe model. Therefore, the full range of uncertainty is captured to the extent that these
distributions encompass the full set of possible values.
A single OPGEE simulation will produce an estimate of total US CH4, but it will not output a
distribution. We run OPGEE 100 times (100 Monte Carlo iterations), each using a different setof equipment-level emission factor distributions (further description in SI section 4.2.1). In
producing variable equipment level emission factor distributions, component counts and fractionof components emitting are approximated as uniform distributions between the maximum andminimum values found in our surveyed studies (see Table S5 and Table S6 for component counts
and Table S10 for fraction leaking). Unfortunately, our sparse dataset does not allow us todetermine a likely distribution shape for these parameters.

Comparison with the EPA GHGI
Equipment leakage
The construction of equipment-level emission factors in the GHGI is rooted in several studies
conducted in the 1990s. We review these studies and trace how emission factors in today’s
GHGI are derived from these earlier analyses. The modelling approach of the early 1990s studies
is closely related to the approach in this paper, in that equipment-level emission factors arecalculated from component-level emissions measurements and counts. By gathering theunderlying datasets used to construct the GHGI’s equipment-level emission factors we cangenerate component-level distributions for comparison with the distributions of our study.
The GHGI relies on a 1996 report by the Gas Research Institute [[64], henceforth referred to as
the “GRI report”] for natural gas systems and a 1996 calculation workbook by the American
Petroleum Institute [[65], henceforth referred to as “API 4638”] for petroleum systems. These
reports were not measurement campaigns, rather these reports summarized the results of multiple
earlier works. The GRI report references API 4589 ([35], sites 9-12) for the Western US natural
gas system and Star Environmental [45] for the Eastern US natural gas system. API 4638
references data from API 4598 (sites 1 – 8). Therefore, only two measurement campaigns
underlie GHGI equipment leakage: the API 4589 and the Star Environmental datasets.
We first analyze the screening data in API 4598 and Star Environmental and follow themethodologies outlined in SI sections 5.2.2 – 5.2.4. In API 4598, screening concentrations from
Appendix C were scanned and tabulated. Unfortunately, it was not possible to re-derive thecomponent-level emission factors in the Star Environmental dataset. This was for two reasons.
First, in the Eastern leak quantification data (provided in Appendix F, [45]), information is notprovided on components measured. Therefore, quantified emissions cannot be connected to the18Non-peer reviewed preprint submitted to EarthArXiv

screening values contained in Appendix E. Second, the Eastern dataset does not report how they
assigned leak volumes to the 81 instrument readings > 10,000 ppmv which were not quantified
with the Hi Flow sampler. Therefore, component-by-component distributions can only begenerated for API 4598.
After digitization and re-engineering of the GHGI methods, we can compare the distributions of
the resulting component-level estimates with our dataset (Figure 4, with additional comparisons in
SI Section 5.2.5).
Tank emissions
To reconstruct emission factors for crude and condensate storage tanks, we begin bydownloading GHGRP data from the “Envirofacts GHG Customized Search” tool [66]. After
gathering the data, we divide the dataset by product stream (natural gas, petroleum systems) andtank class. However, before making any comparisons with this study, we need to adjust how
emissions-factors are reported by the GHGI. The GHGI reports storage tank emission factors ona throughput-basis (kgCH4/bbl/year) and our study reports emission factors on a tank basis(kgCH4/tank/day). Fortunately, in addition to tank throughput, atmospheric storage tank counts
per sub-basin are also reported to the GHGRP by tank class.
Emissions-factor distributions (Figure 5) are calculated by dividing total emissions by tank countfor every sub-basin (or row in the downloaded dataset). See SI Section 5.3 for additional details
on this calculation. In SI Section 5.3, we validate this approach by calculating and comparing
throughput-basis emission factors with those reported in the GHGI.

Acknowledgements
This work was funded by the California Air Resources Board, grant 18ISD011. Support for the work was also
provided by Novim under a Limited Sponsorship Agreement with the Joint Institute for Strategic Energy Analysis of
NREL.

References[1]T. F. Stocker et al., Climate change 2013 the physical science basis: Working Group I contribution to thefifth assessment report of the intergovernmental panel on climate change. 2013.

[2](EPA) Environmental Protection Agency, “Overview of Greenhouse Gases.”
https://www.epa.gov/ghgemissions/overview-greenhouse-gases.

[3]

Q. Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A.,
Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P.,
Blake, D. R., Brailsford, G., Bruhwiler, L., “The Global Methane Budget 2000–2017,” Earth Syst. Sci. Data
Discuss, doi: https://doi.org/10.5194/essd-2019-128.

[4]

P. Friedlingstein et al., “Global carbon budget 2019,” Earth Syst. Sci. Data, 2019, doi: 10.5194/essd-111783-2019.

[5]

R. W. Howarth, R. Santoro, and A. Ingraffea, “Methane and the greenhouse-gas footprint of natural gas
from shale formations,” Clim. Change, vol. 106, no. 4, p. 679, 2011.

19

Non-peer reviewed preprint submitted to EarthArXiv[6]

R. W. Howarth, “A bridge to nowhere: methane emissions and the greenhouse gas footprint of natural gas,”
Energy Sci. Eng., vol. 2, no. 2, pp. 47–60, 2014.

[7]

L. M. Cathles, L. Brown, M. Taam, and A. Hunter, “A commentary on ‘The greenhouse-gas footprint of
natural gas in shale formations’ by R.W. Howarth, R. Santoro, and Anthony Ingraffea,” Clim. Change,
2012, doi: 10.1007/s10584-011-0333-0.

[8]

R. A. Alvarez, S. W. Pacala, J. J. Winebrake, W. L. Chameides, and S. P. Hamburg, “Greater focus neededon methane leakage from natural gas infrastructure,” Proc. Natl. Acad. Sci., 2012.

[9](EPA) Environmental Protection Agency, “Inventory of Greenhouse Gas Emissions and Sinks,” 2019.

[10]

B. Hmiel et al., “Preindustrial 14CH4 indicates greater anthropogenic fossil CH4 emissions,” Nature, 2020,
doi: 10.1038/s41586-020-1991-8.

[11]

A. R. Brandt et al., “Methane leaks from North American natural gas systems,” Science (80-. )., vol. 343,
no. 6172, pp. 733–735, 2014.

[12]

D. Zavala-Araiza et al., “Reconciling divergent estimates of oil and gas methane emissions,” Proc. Natl.
Acad. Sci. U. S. A., 2015, doi: 10.1073/pnas.1522126112.

[13]

R. A. Alvarez et al., “Assessment of methane emissions from the US oil and gas supply chain,” Science (80. )., p. eaar7204, 2018.

[14]

A. Karion et al., “Methane emissions estimate from airborne measurements over a western United States
natural gas field,” Geophys. Res. Lett., 2013, doi: 10.1002/grl.50811.

[15]

J. Peischl et al., “Quantifying atmospheric methane emissions from oil and natural gas production in the
Bakken shale region of North Dakota,” J. Geophys. Res., 2016, doi: 10.1002/2015JD024631.

[16]

J. Peischl et al., “Quantifying atmospheric methane emissions from the Haynesville, Fayetteville, andnortheastern Marcellus shale gas production regions,” J. Geophys. Res., 2015, doi: 10.1002/2014JD022697.

[17]

S. M. Miller et al., “Anthropogenic emissions of methane in the United States,” Proc. Natl. Acad. Sci. U. S.
A., 2013, doi: 10.1073/pnas.1314392110.

[18]

S. Schwietzke et al., “Improved Mechanistic Understanding of Natural Gas Methane Emissions from
Spatially Resolved Aircraft Measurements,” Environ. Sci. Technol., 2017, doi: 10.1021/acs.est.7b01810.

[19]

G. Pétron et al., “A new look at methane and nonmethane hydrocarbon emissions from oil and natural gas
operations in the Colorado Denver-Julesburg Basin,” J. Geophys. Res., 2014, doi: 10.1002/2013JD021272.

[20]

A. Karion et al., “Aircraft-Based Estimate of Total Methane Emissions from the Barnett Shale Region,”
Environ. Sci. Technol., 2015, doi: 10.1021/acs.est.5b00217.

[21]

R. Harriss, R. A. Alvarez, D. Lyon, D. Zavala-Araiza, D. Nelson, and S. P. Hamburg, “Using Multi-Scale
Measurements to Improve Methane Emission Estimates from Oil and Gas Operations in the Barnett Shale
Region, Texas,” Environmental Science and Technology. 2015, doi: 10.1021/acs.est.5b02305.

[22]

T. I. Yacovitch et al., “Mobile Laboratory Observations of Methane Emissions in the Barnett Shale Region,”
Environ. Sci. Technol., 2015, doi: 10.1021/es506352j.

[23]

C. W. Rella, T. R. Tsai, C. G. Botkin, E. R. Crosson, and D. Steele, “Measuring emissions from oil andnatural gas well pads using the mobile flux plane technique,” Environ. Sci. Technol., 2015, doi:
10.1021/acs.est.5b00099.

[24]

H. L. Brantley, E. D. Thoma, W. C. Squier, B. B. Guven, and D. Lyon, “Assessment of methane emissions
from oil and gas production pads using mobile measurements,” Environ. Sci. Technol., 2014, doi:
10.1021/es503070q.

[25](EPA) Environmental Protection Agency, “Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 2018,” 2020.

20

Non-peer reviewed preprint submitted to EarthArXiv[26]

The White House, “Climate action plan: Strategy to reduce methane emissions,” in Methane: Emission
Sources and Reduction Strategies, 2015.

[27]

J. Penman, M. Gytarsky, T. Hiraishi, W. Irving, and T. Krug, 2006 IPCC - Guidelines for National
Greenhouse Gas Inventories. 2006.

[28]

Heath, G., Warner, E., Steinberg, D., Brandt, A.R., “Estimating U.S. Methane Emissions from the Natural
Gas Supply Chain: Approaches, Uncertainties, Current Estimates, and Future Studies,” Golden, CO, 2015.

[29]

Office of Inspector General EPA, “EPA Needs to Improve Air Emissions Data for the Oil and Natural Gas
Production Sector,” 2013.

[30]

A. R. Brandt, G. A. Heath, and D. Cooley, “Methane Leaks from Natural Gas Systems Follow Extreme
Distributions,” Environ. Sci. Technol., 2016, doi: 10.1021/acs.est.6b04303.

[31]

T. L. Vaughn et al., “Temporal variability largely explains top-down/bottom-up difference in methane
emission estimates from a natural gas production region,” Proc. Natl. Acad. Sci. U. S. A., 2018, doi:
10.1073/pnas.1805687115.

[32]

D. Zavala-Araiza et al., “Super-emitters in natural gas infrastructure are caused by abnormal process
conditions,” Nat. Commun., 2017, doi: 10.1038/ncomms14012.

[33]

D. T. Allen et al., “Measurements of methane emissions at natural gas production sites in the United States,”
Proc. Natl. Acad. Sci. U. S. A., 2013, doi: 10.1073/pnas.1304880110.

[34]

M. Omara et al., “Methane Emissions from Natural Gas Production Sites in the United States: Data
Synthesis and National Estimate,” Environ. Sci. Technol., vol. 52, no. 21, pp. 12915–12925, 2018.

[35]

Star Environmental, “Fugitive hydrocarbon emissions from oil and gas production operations. API
Publication 4589,” 1993.

[36]

A. P. Pacsi et al., “Equipment leak detection and quantification at 67 oil and gas sites in the Western UnitedStates,” Elementa, 2019, doi: 10.1525/elementa.368.

[37]

Clearstone Engineering Ltd., “Update of Equipment, Component and Fugitive Emission Factors for Alberta
Upstream Oil and Gas,” Calgary, AB, 2018.

[38](ERG) Eastern Research Group, “City of Fort Worth Natural Gas Air Quality Study,” Morrisville, NC,
2011.

[39]

Gas Technology Institute, “Gas Resource Database: Unconventional Natural Gas and Gas Composition
Databases,” 2001.

[40]

Enervus, “Enervus Exploration and Production.” https://www.enverus.com/industry/exploration-andproduction/.

[41]

D. Zavala-Araiza et al., “Toward a Functional Definition of Methane Super-Emitters: Application to Natural
Gas Production Sites,” Environ. Sci. Technol., 2015, doi: 10.1021/acs.est.5b00133.

[42]

D. R. Lyon, R. A. Alvarez, D. Zavala-Araiza, A. R. Brandt, R. B. Jackson, and S. P. Hamburg, “Aerial
Surveys of Elevated Hydrocarbon Emissions from Oil and Gas Production Sites,” Environ. Sci. Technol.,
2016, doi: 10.1021/acs.est.6b00705.

[43]

J. Campbell, L., Campbell, M., Cowgill, M., Epperson, D., Hall, M., Harrison, M., Hummel, K., Myers, D.,
Shires, T., Stapper, B., Stapper, C., Wessel, “Methane emissions from the natural gas industry. Volume 11:
Compressor driver exhaust,” 1996.

[44]

New Source Performance Standard, “Stationary Engines: SI Engines (NSPS). C.F.R., Title 40, Part 60,
Subpart JJJJ.” 2008, [Online]. Available:
https://www.dieselnet.com/standards/us/stationary_nsps_si.php#ng.

[45]

Star Environmental, “Fugitive Hydrocarbon Emissions: Eastern Gas Wells,” 1995.

21

Non-peer reviewed preprint submitted to EarthArXiv[46](EPA) Environmental Protection Agency, “Protocol for Equipment Leak Emission Estimates. Report No.
EPA-453/R-95-017,” 1995.

[47]

Star Environmental, “Emission factors for oil and gas production operations. API Publication 4615,” 1995.

[48]

Code of Federal Regulations, Title 40 Part 98 Subpart W, Petroleum and Natural Gas Systems. 2010.

[49](API) American Petroleum Institute, “PRODUCTION TANK EMISSIONS MODEL - A PROGRAM FOR
ESTIMATING EMISSIONS FROM HYDROCARBON PRODUCTION TANKS - E&P TANK VERSION
2.0,” 2000.

[50]

aspentech, “HYSYS 2004 Simulation basis,” 2004.

[51](ERG) Eastern Research Group, “Condensate Tank Oil and Gas Activities,” 2012.

[52]

S. N. Lyman, T. Tran, M. L. Mansfield, and A. P. Ravikumar, “Aerial and ground-based optical gas imaging
survey of Uinta Basin oil and gas wells,” Elementa, 2019, doi: 10.1525/elementa.381.

[53]

B. Mansfield, Marc L., Lyman, S., O’Neil, T., Anderson, R., Jones, C., Tran, H., Mathis, J., Barickman, P.,
Oswald, W., LeBaron, “STORAGE TANK EMISSIONS PILOT PROJECT (STEPP): FUGITIVEORGANIC COMPOUND EMISSIONS FROM LIQUID STORAGE TANKS IN THE UINTA BASIN,”
2017.

[54]

J. G. Englander, A. R. Brandt, S. Conley, D. R. Lyon, and R. B. Jackson, “Aerial Interyear Comparison and
Quantification of Methane Emissions Persistence in the Bakken Formation of North Dakota, USA,”
Environ. Sci. Technol., 2018, doi: 10.1021/acs.est.8b01665.

[55]

A. Bradbury, J., Obeiter, M., Draucker, L., Wang, W., Stevens, “Clearing the air: reducing upstream
greenhouse gas emissions from US natural gas systems,” 2013.

[56]

ICF International, “Economic Analysis of Methane Emission Reduction Opportunities in the US Onshore
Oil and Natural Gas Industries,” Fairfax, VA, 2014.

[57]

Y. Gan et al., “Carbon footprint of global natural gas supplies to China,” Nat. Commun., 2020, doi:
10.1038/s41467-020-14606-4.

[58]

G. et al Domke, “2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
Vol 4. Chapter 4. Forest Land,” Forestry, 2019, doi: 10.1016/j.phrs.2011.03.002.

[59]

D. T. Allen et al., “Methane emissions from process equipment at natural gas production sites in the UnitedStates: Pneumatic controllers,” Environ. Sci. Technol., 2015, doi: 10.1021/es5040156.

[60]

C. S. Bell et al., “Comparison of methane emission estimates from multiple measurement techniques atnatural gas production pads,” Elementa, 2017, doi: 10.1525/elementa.266.

[61]

E. D. Thoma, P. Deshmukh, R. Logan, M. Stovern, C. Dresser, and H. L. Brantley, “Assessment of Uinta
Basin Oil and Natural Gas Well Pad Pneumatic Controller Emissions,” J. Environ. Prot. (Irvine,. Calif).,2017, doi: 10.4236/jep.2017.84029.

[62](EPA) Environmental Protection Agency, “Additional Revisions Considered for 2018 and Future GHGIs,”
2018. [Online]. Available: www.epa.gov/sites/production/files/201804/documents/ghgemissions_additional_revisions_2018.pdf.

[63](EPA) Environmental Protection Agency, “Revision to Well Counts Data,” 2015.
https://www.epa.gov/sites/production/files/2015-12/documents/revision-data-source-well-counts-4-102015.pdf.

[64]

M. R. Hummel, K.E., Campbell, L.M. and Harrison, “Methane Emissions from the Natural Gas Industry.
Volume 8. Equipment Leaks,” 1996.

[65]

Star Environmental, “Calculation Workbook for Oil and Gas Production Equipment Fugitive Emissions.
API Publication 4638,” 1996.

22

Non-peer reviewed preprint submitted to EarthArXiv[66](EPA) Environmental Protection Agency, “Greenhouse gas customized search.”https://www.epa.gov/enviro/greenhouse-gas-customized-search.

23